This is done via the MIRA algorithm (Crammer and Singer, 2003).

if the current parameters are θ t , and we enforce constraint Equation 5 for a particular pair y, y , then we will find θ t+1 that minimizes The updates can be calculated in closed form.


It is easy to extend our sampling-based parsing framework to joint prediction of parsing and other labels.

when sampling the new heads, we can also sample the values of other variables at the same time.


• Valency We consider valency features for each POS tag.

we add two types of valency information: (1) the binned number of non-punctuation modifiers and (2) the concatenated POS string of all those modifiers.


Our system not only outperforms the best single system (Björkelund et al., 2013) by 1.4%, but it also tops the ensemble system that combines three powerful parsers: the Mate parser (Bohnet, 2010), the Easy-First parser (Goldberg and Elhadad, 2010) and the Turbo parser (Martins et al., 2013) Impact of Sampling Methods We compare two sampling methods introduced in Section 3.2 with respect to their decoding efficiency.

we measure the score of the retrieved trees in testing as a function of the decoding speed, measured by the number of tokens per second.


Typically, each successive grammar G is a refinement of the preceding grammar G −1 .

for each symbol A x in the fine grammar, there is some symbol A in the coarse grammar.


In scaling, floating point numbers are paired with an additional number that extends the exponent.

the number is represented as f = f • exp(s).


Thus, we can use the coarse pass's inside and outside scores as the scaling values for the fine pass's scores.

in addition to computing a pruning mask, in the coarse pass we store the maximum inside and outside score in each span, giving two arrays of scores s I i,j and s O i,j .


These slight differences arise from the usual minor variation in implementation details.

we use one coarse pass instead of several, and a different MBR algorithm.


The fourth block reports performance in what might be the most realistic scenario, namely by tuning the parameters on a development task.

we pick the models that work best on the small rg set, and report their performance on all tasks (we obtained similar results by picking other tuning sets).


The phrase broken promise is an AN metaphor, where attributes from a concrete domain (associated with the concrete word broken) are transferred to a more abstract domain, which is represented by the relatively abstract word promise.

we map an abstract concept promise to a concrete domain of physical things, where things can be literally broken to pieces.


Vector space word representations learned using unsupervised algorithms are often effective features in supervised learning methods (Turian et al., 2010).

many such representations are designed to capture lexical semantic properties and are quite effective features in semantic processing, including named entity recognition (Turian et al., 2009), word sense disambiguation (Huang et al., 2012), and lexical entailment (Baroni et al., 2012).


We view this result as a strong evidence of language-independent nature of our metaphor detection method.

this shows that proposed conceptual features can be used to detect selectional preferences violation across languages.


Conversely, there are also senses in Macmillan which aren't attested in the annotated sample of usages.

of the 112 senses defined for the 20 target lemmas, 25 (= 22.3%) of the senses are not attested in the 2000 usages in either corpora.


for presentation to a lexicographer as part of a dictionary update process (Rundell and Kilgarriff, 2011;.

for each usage, we want to classify whether it is an instance of a given novel sense.


To that end, we chose to include only instances from lemmas with a removed sense, and repeated the experiment for the medium-and high-frequency novel sense condition (for the lowfrequency condition, all target lemmas have a novel sense).

we are assuming knowledge of which words have novel sense, and the task is to identify specifically what the novel sense is, as represented by novel usages.


Depending on the available validation function V (as defined in Section 4.2), we can also accurately prune the beam for the computation of the first half of the gradient.

when assuming access to labeled equations, we can constrain the search to consider only partial hypotheses that could possibly be completed to produce the labeled equations.


To ensure each problem type is seen at least a few times in the training data, we removed the infrequent problem types.

we induced the system template from each equation system, as described in Section 4.1, and removed all problems for which the associated system template appeared less than 6 times in the dataset.


More precisely, for each X ∈ N ∪ W a PCFG associates distributions G X over the set of trees T X generated by X as follows: If X ∈ W (i.e., if X is a terminal) then G X is the distribution that puts probability 1 on the single-node tree labelled X.

if X ∈ N (i.e., if X is a nonterminal) then: where R X is the subset of rules in R expanding nonterminal X ∈ N , and: TD X (G 1 , .


(2007b) explain, by inserting a Dirichlet Process (DP) or Pitman-Yor Process (PYP) into the generative mechanism (1) the model "concentrates" mass on a subset of trees (Teh et al., 2006).

an Adaptor Grammar identifies a subset A ⊆ N of adapted nonterminals.


(We put "function words" in scare quotes below because our model only approximately captures the linguistic properties of function words).

we replace rules (19-21) with the following sequence of rules: This model memoises (i.e., learns) both the individual "function words" and the sequences of "function words" that modify the Colloc1 − Colloc3 constituents.


2 While Bayesian model selection is in principle straight-forward, it turns out to require the ratio of two integrals (for the "evidence" or marginal likelihood) that are often intractable to compute.

given a training corpus D of unsegmented sentences and model families G 1 and G 2 (here the "function word" adaptor grammars with left-peripheral and right-peripheral attachment respectively), the Bayes factor K is the ratio of the marginal likelihoods of the data: where the marginal likelihood or "evidence" for a model G is obtained by integrating over all of the hidden or latent structure and parameters θ: Here the variable θ ranges over the space ∆ of all possible parses for the utterances in D and all possible configurations of the Pitman-Yor processes and their parameters that constitute the "state" of the Adaptor Grammar G. While the probability of any specific Adaptor Grammar configuration θ is not too hard to calculate (the MCMC sampler for Adaptor Grammars can print this after each sweep through D), the integral in (31) is in general intractable.


By examining sentiment compositions of negators and arguments, we model the quantitative behavior of negators in changing sentiment.

given a negated phrase (e.g., isn't very good) and the sentiment score of its argument (e.g., s("very good ′′ ) = 0.5), we focus on understanding the negator's quantitative behavior in yielding the sentiment score of the negated phrase s("isn ′ t very good ′′ ).


In general, we can simply define this category of models in Equation 1.

the model parameters are only based on the sentiment value of the arguments.


The most straightforward way of expanding the non-lexicalized heuristics is probably to make the models to be dependent on the negators.

negator-based shifting We can simply extend the basic shifting model above to consider the lexical information of negators: each negator has its own C. We call this model negator-based shifting.


We consider two models in this study: one drops s( w) in Equation 4 and directly models f (w n , r( w)).

the non-uniform information shown in Figure 1 is not directly modeled.


To train the model, one first needs to calculate the predicted sentiment distribution for each node: and then compute the posterior probability over the m labels: During learning, following the method used by the RNTN model in (Socher et al., 2013), PSTN also aims to minimize the cross-entropy error between the predicted distribution y i ∈ R m×1 at node i and the target distribution t i ∈ R m×1 at that node.

the error for a sentence is calculated as: where, λ represents the regularization hyperparameters, and j ∈ m denotes the j-th element of the multinomial target distribution.


To minimize E(θ), the gradient of the objective function with respect to each of the parameters in θ is calculated efficiently via backpropagation through structure, as proposed by Goller and Kchler (1996).

we first compute the prediction errors in all tree nodes bottom-up.


(2013) used to map real-valued sentiment scores to sentiment categories.

we conduct the mapping with the formula: p real i = y i • [0.1 0.3 0.5 0.7 0.9]; i.e., we calculate the dot product of the posterior probability y i and the scaling vector.


First, we operate over a heterogeneous graph to model semantic relations and opinion relations into a unified model.

our heterogeneous graph is composed of three subgraphs which model different relation types and candidates, as shown in Figure 1.


For example, "price" and "expensive" are topically related in phone's domain, so they are a word preference for each other.

we use a vector P to represent word preference of the ith opinion target candidate.


It allows that the candidate confidence propagates to other candidates only in its preference cluster.

we make modification on original transition matrix M to = (M to 1 , M to 2 , ..., M to |V t | ) and add each candidate's preference in it.


In contrast, our approach allows structured modeling of sentiment while taking into account both local and global contextual information.

we encode intuitive lexical and discourse knowledge as expressive constraints and integrate them into the learning of conditional random field models via posterior regularization.


In this paper, we propose a sentence-level sentiment classification method that can (1) incorporate rich discourse information at both local and global levels; (2) encode discourse knowledge as soft constraints during learning; (3) make use of unlabeled data to enhance learning.

we use the Conditional Random Field (CRF) model as the learner for sentence-level sentiment classification, and incorporate rich discourse and lexical knowledge as soft constraints into the learning of CRF parameters via Posterior Regularization (PR) (Ganchev et al., 2010).


We develop a rich set of context-aware posterior constraints for sentence-level sentiment analysis by exploiting lexical and discourse knowledge.

we construct the lexical constraints by extracting sentiment-bearing patterns within sentences and construct the discourse-level constraints by extracting discourse relations that indicate sentiment coherence or sentiment changes both within and across sentences.


This con-firms that encoding lexical and discourse knowledge as posterior constraints allows the featurebased model to gain additional learning power for sentence-level sentiment prediction.

incorporating discourse constraints leads to consistent improvements to our model.


This basic formulation is impractical because it is proportional to m. A hierarchical softmax approximation can be applied to reduce the computational cost to log 2 (m), see (Morin and Bengio, 2005) for details.

to alleviate the data sparsity problem, EB is first trained on a very large corpus 3 (denoted by C), and then fine-tuned on the target review corpus R. for phrasal product features, a statistic-based method in (Zhu et al., 2009) is used to detect noun phrases in R. then, an Unfolding Recursive Autoencoder (Socher et al., 2011) is trained on C to obtain embedding vectors for noun phrases.


We propose an adapted version on the random walking view of the Adsorption algorithm (Baluja et al., 2008) by updating the following formula until L converges, where M is the semantic transition matrix built from G; D = Diag[log tf (t)] is a diagonal matrix of log frequencies, which is designed to assign higher "confidence" scores to more frequent seeds; and α is a balancing parameter.

when α = 0, we can set the prior knowledge I without V c to L 0 so that only lexical semantic clue is used; otherwise if α = 1, only contextual semantic clue is used.


Extraction guided by learned knowledge: For reliable aspect extraction using the learned prior knowledge, we must account for possible errors in the knowledge.

a piece of automatically learned knowledge may be wrong or domain specific (i.e., the words in the knowledge are semantically coherent in some domains but not in others).


(2013) to generate search queries by performing a breadth-first search over the query space.

we use the Google Suggest API, which takes a partial query (e.g., "list of movies") and outputs several complete queries (e.g., "list of horror movies").


To train the model, we find a parameter vector θ that maximizes the regularized log marginal probability of the compatibility function being satisfied.

given training data D = where Note that c( z w ) = 1 when the entity list y = z w selected by z is compatible with the annotation; otherwise, c( z w ) = 0.


As discussed in (Power and Third, 2010), one important limitation of these approaches is that they assume a simple deterministic mapping between knowledge representation languages and some controlled natural language (CNL).

the assumption is that each atomic term (individual, class, property) maps to a word and each axiom maps to a sentence.


The KBGen task was introduced as a new shared task at Generation Challenges 2013 (Banik et al., 2013) 1 and aimed to compare different generation systems on KB data.

the task is to verbalise a subset of a knowledge base.


e.g., Figure 4) is a Feature-Based Tree Adjoining Grammar with a Unification-based compositional semantics as described in (Gardent and Kallmeyer, 2003).

our grammars differs from the traditional probabilistic Tree Adjoining Grammar extracted as described in e.g., (Chiang, 2000) in that they encode both syntax and semantics rather than just syntax.


To get the DRS simplification model, we combine the probability of splitting with the probability of deletion: where θ is a sequence of simplification operations and str(θ(D c )) is the sequence of words associated with a DRS resulting from simplifying D c using θ.

the probability of a splitting operation for a given DRS D c is:  if the DRS is split on the splitting candidate sp cand , the probability of the split is then given by the SPLIt table (table 1) for the isSplit value "true" and the split candidate sp cand ; else it is the product of the probability given by the SPLIt table for the isSplit value "false" for all split candidate considered for D c .


While our system splits in proportion similar to that observed in the training data, the other systems either split very often (80% of the time for Zhu and 63% of the time for Woodsend) or not at all (Wubben).

when compared to the other systems, our system performs splits in proportion closest to the reference both in terms of total number of splits and of average number of splits per sentence.


The difference between our system and both Zhu and Woodsend system is significant.

zhu's output is judged less fluent probably because of the many incorrect splits it licenses.


For example, several computational grammar formalisms, such as Lexical Function Grammar (LFG; Bresnan and Kaplan, 1982;Dalrymple, 2001) and Head-driven Phrase Structure Grammar (HPSG; Pollard and Sag, 1994) encode grammatical functions directly.

gRs can be viewed as the dependency backbone of an LFg analysis that provide general linguistic insights, and have great potential advantages for NLP applications, (Kaplan et al., 2004;Briscoe and Carroll, 2006;Clark and Curran, 2007a;Miyao et al., 2007).


We take into account both theoretical assumptions and analyzing practices to enrich coindexation information according to phrasestructure patterns.

we try to link an anaphoric EC e with its c-commonders if no non-empty antecedent has already been coindexed with e. Because the CTB is influenced deeply by the X-bar syntax, which regulates constituent analysis much, the number of our linguistic rules is quite modest.


Linguistic experts carefully examine the corresponding GR graphs derived by our extraction algorithm and correct all errors.

a gold standard GR annotation set is created.


In this section, we investigate the feasibility of applying a datadriven, grammar-free approach to build GRs directly.

transition-based dependency parsing method is studied.


We also borrow many ideas from recent advances in deep syntactic or semantic parsing for English.


For an unlabeled instance, the model is updated to maximize the probability of its parse forest, instead of a single parse tree in traditional tri-training.

the model is free to distribute probability mass among the trees in the parse forest to its liking, as long as the likelihood improves (Täckström et al., 2013).


Detailed analysis demonstrates the effectiveness of our approach.

we find that our approach is very effective when using divergent parsers such as the generative parser, and it is also helpful to properly balance the size and oracle accuracy of the parse forest of the unlabeled data.


The guidelines given in both cases were the same: "Do you think the two words are semantically close ?

is there a semantic relation between them, either classical (synonymy, hypernymy, co-hyponymy, meronymy, comeronymy) or not (the relation can be paraphrased but does not belong to the previous cases) ?"


The JNNSE algorithm is advantageous in that it can handle partially paired data.

the algorithm does not require that every row in X also have a row in Y .


Two agents learn concurrently by interacting with each other without any need for simulated users (SUs) to train against or corpora to learn from.

we compare the Qlearning, Policy Hill-Climbing (PHC) and Win or Learn Fast Policy Hill-Climbing (PHC-WoLF) algorithms, varying the scenario complexity (state space size), the number of training episodes, the learning rate, and the exploration rate.


We also vary the exploration rate per epoch.

in the experiments reported in section 5.1 the exploration rate is set as follows: 0.95 for epoch 1, 0.8 for epoch 2, 0.5 for epoch 3, 0.3 for epoch 4, and 0.1 for epoch 5.


Second, by using two linear-chain CRFs to label a sequence of discourse constituents, we can incorporate contextual information in a more natural way, compared to using traditional discriminative classifiers, such as SVMs.

in the Viterbi decoding of the first CRF, we include additional constraints elicited from common sense, to make more effective local decisions.


It adopts a pipeline framework, and greedily builds the discourse tree from the bottom up.

starting from EDUs, at each step of the tree-building, a binary SVM classifier is first applied to determine which pair of adjacent discourse constituents should be merged to form a larger span, and another multi-class SVM classifier is then applied to assign the type of discourse relation that holds between the chosen pair.


First, they decomposed the problem of textlevel discourse parsing into two stages: intrasentential parsing to produce a discourse tree for each sentence, followed by multi-sentential parsing to combine the sentence-level discourse trees and produce the text-level discourse tree.

they employed two separate models for intra-and multi-sentential parsing.


4 Bottom-up tree-building For both intra-and multi-sentential parsing, our bottom-up tree-building process adopts a similar greedy pipeline framework like the HILDA discourse parser (discussed in Section 2.1), to guarantee efficiency for large documents.

starting from the constituents on the bottom level (EDUs for intra-sentential parsing and sentence-level discourse trees for multi-sentential parsing), at each step of the tree-building, we greedily merge a pair of adjacent discourse constituents such that the merged constituent has the highest probability as predicted by our structure model.


Therefore, to improve its accuracy, we enforce additional commonsense constraints in its Viterbi decoding.

we disallow 1-1 transitions between adjacent labels (a discourse unit can be merged with at most one adjacent unit), and we disallow all-zero sequences (at least one pair must be merged).


In this paper, we propose a graph model to represent the contextual discourse information from both lexical and topic perspectives.

a word-based graph model is proposed to represent the explicit relatedness among words in a discourse from the lexical perspective, while a topic-driven word-based model is proposed to enrich the implicit relatedness between words, by adding one more layer to the word-based graph model in representing the global topic distribution of the whole dataset.


Given the graph models and the PageRank optimization algorithm discussed above, four kinds of contextual discourse information are extracted as inter-sentence features (Table 2).

the total weight and the max weight of words in the focus candidate are calculated as follows: where i represents the content words in the focus candidate.


As a freely downloadable resource, the *SEM shared task corpus is annotated on top of PropBank, which uses the WSJ section of the Penn TreeBank.

negation focus annotation on this corpus is restricted to verbal negations (with corresponding mark MNEG in PropBank).


Our framework can greatly reduce the human effort for building a domainspecific sentiment lexicon with high quality.

in our evaluation, working with just 20 manually labeled reviews, it generates a domain-specific sentiment lexicon that yields weighted average F-Measure gains of 3%.


ReNew can greatly reduce the human effort for building a domain-specific sentiment lexicon with high quality.

in our evaluation on two real datasets, working with just 20 manually labeled reviews, ReNew generates a domainspecific sentiment lexicon that yields weighted average F-Measure gains of 3%.


The LI promotes triples by respecting mutual exclusion and the existing lexicon.

it does not promote triples if they exist in multiple sentiment categories or if they already belong to a different sentiment category.


The feature OD yields the best accuracy, followed by G, P, and T. Although T yields the worst accuracy, incorporating it improves the resulting accuracy of the other features, as shown by the dark grey bars.

the accuracy of OD is markedly improved by adding T. The model trained using all the feature sets yields the best accuracy.


Thus we use as a reward a measure of the match between the semantic annotation of the partial tree and the communicative goal.

the larger the overlap between the predicates, the higher the reward.


First, because we receive frequent, reasonably accurate feedback, we favor breadth over depth in the tree search.

it is more important in our case to try a variety of actions than to pursue a single action very deep.


This conclusion is also validated by our preliminary experiments on the task of active learning relation extraction, thus we adopt the LC metric for simplicity.

with a sequence of K probabilities for a relation instance at some iteration, denoted as {p 1 ,p 2 ,…p K } in the descending order, the LC metric of the relation instance can be simply picked as the first one, i.e.


SL-CR (Supervised Learning with crosslingual labeled instances): in addition to monolingual labeled instances (SL-MO), the training data for supervised learning contain labeled instances translated from the other language.

the initial training data contain L c and L ct for Chinese, or L e and L et for English.


the data contain L c and U c for Chinese, or L e and U e for English respectively.


Table 1 compares the deficiency scores of relation classification on the Chinese (ACE2005c) and English corpora (ACE2005e) for various learning methods, i.e., SL-CR, AL-MO, AL-CR and AL-BI.

sL-MO is used as the baseline system against which deficiency scores for other methods are computed.


The figures clearly demonstrate the performance difference for both languages among five methods at the beginning of iterations while F1scores converge at the end of iterations.

at the very outset, AL-BI outperforms other methods, quickly jumps to a very high point comparable to its best performance.


Substituting, we have except the constraint that µ = −c − λ implies that for µ to be positive λ ≤ c. Since this Lagrangian has the same form as equation 3, we can also derive a dual problem, which is the same as in equation 4, with the additional constraint that each λ i can not be bigger than its cost c i .

the dual problem can not penalize the violation of a constraint more than the soft constraint model in the primal would penalize you if you violated it.


In this paper, we focus on addressing the limitations caused by the imperfect mapping results and study how to further improve the retrieval performance of the concept-based ranking methods.

we apply axiomatic approaches and propose two weighting regularization methods that adjust the weighting based on the relations among the concepts.


In this paper, to address the limitation caused by the inaccurate concept mapping results, we propose to regularize the weighting strategies in the concept-based representation methods.

by applying the axiomatic approaches (Fang and Zhai, 2005), we analyze the retrieval func-tions with concept-based representation and find that they may violate some reasonable retrieval constraints.


In this paper, we focus on the visit-level relevance because of its simplicity.

given a patient's visit, all the medical records generated from this visit are merged as a document.


Unified Medical Language System (UMLS) is a metathesaurus containing information from more than 100 controlled medical terminologies such as the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT) and Medical Subject Headings (MeSH).

it contains the information about over 2.8 million biomedical concepts.


Moreover, NLP tools for utilizing the information from UMLS have been developed.

metamap (Aronson, 2001) can take a text string as the input, segment it into phrases, and then map each phrase to multiple UmLS CUIs with confidence scores.


To overcome this limitation, concept-based representation has been proposed to bridge the vocabulary gap between documents and queries (Qi and Laquerre, 2012;Limsopatham et al., 2013b;Koopman et al., 2012).

metamap is used to map terms from queries and documents (e.g., medical records) to the semantic concepts from biomedical knowledge bases such as UmLS.


Although MetaMap is able to rank all the candidate concepts with the confidence score and pick the most likely one, the accuracy is not very high.

our preliminary results show that turning on the disambiguation functionality provided by MetaMap (i.e., returning only the most likely concept for each query) could lead to worse retrieval performance than using all the candidate mappings.


To address the limitations caused by the inaccurate mapping results, we propose to apply axiomatic approaches (Fang and Zhai, 2005) to regularize the weighting strategies for concept-based representation methods.

we first formalize retrieval constraints that any reasonable concept-based representation methods should satisfy and then discuss how to regularize the existing weighting strategies to satisfy the constraints and improve the retrieval performance.


We con-duct exploratory data analysis on these three options.

for each option, we generate a plot indicating the correlation between the IDF value of a concept and the relevance probability of the concept (i.e., the probability that a document containing the concept is relevant).


This regularization function aims to penalize the weight of concept e based on its variants as well as the concepts from other aspects.

a concept would receive more penalty (i.e., its weight will be decreased more) when it has more variants and the mappings of these variants are more accurate.


In this paper, we present a general methodology that can use axiomatic approaches as guidance to regularize the concept weighting strategies to address the limitations caused by the inaccurate concept mapping and improve the retrieval performance.

we proposed two weighting regularization methods based on the relations among concepts.


, cL), Chemometrics (Geladi and Kowalski, 1986), producing stable prediction models even when the number of samples is considerably smaller than the dimensionality of the feature space.

pLSR fits a smaller number of latent variables (10 − 100 in practice) such that the correlation between the feature vectors for pivots in the two domains are maximised in this latent space.


manually POS tagged) sentence, we select its neighbours u (i) in the source domain as additional features.

we measure the similarity, sim(u , between the source domain distributions of u (i) and w, and select the top r similar neighbours u (i) for each word w as additional features for w. We refer to such features as distributional features in this work.


Note that given a test review, we find the distributional features that are similar to all the words in the test review from the source domain.

we do not find distributional features independently for each word in the test review.


The NA (no-adapt) baseline simulates the effect of not performing any DA.

in POS tagging, a CRF trained on source domain labeled sentences is applied to target domain test sentences, whereas in sentiment classification, a logistic regression classifier trained using source domain labeled reviews is applied to the target domain test reviews.


Next, we use the AN-N dataset of Dinu and colleagues for a more interesting evaluation of one-step decomposition.

we reverse the original paraphrasing direction by attempting to generate, for example, personal charm from charisma.


It can be argued that to be sustainable, inductive aspects of meaning must be recurrent enough to be learnable by new users.

a noncompositional phrase such as 'kick the bucket' is likely to persist in common parlance only if it is frequently used with its associated semantic mapping.


This motivates the need for a frequency-driven view of lexical semantics.

such a perspective can be especially advantageous for distributional semantics for reasons we outline below.


While the two sentences in consideration have near-identical syntax and could be argued to have semantically aligned words in similar positions, the semantics of the complete sentences are widely divergent.

the 'bag of words' assumption in tree kernels doesn't suffice for these lexemes, and a stronger semantic model is needed to capture phrasal semantics as well as diverging inter-word relations such as in 'coffee table' and 'water table'.


Other methods have attempted to exploit morphological, syntactic and semantic characteristics of MWEs.

approaches such as Bannard (2007) use syntactic rigidity to characterize MWEs.


It is worthwhile to point out that the task of motif segmentation is slightly different from MWE identification.

the onus on recurrent occurrences means that nondecomposibility is not an essential consideration for a word to be considered a motif.


We also associate a penalizing cost for each non unary-motif to avoid aggressive agglutination of tokens.

for an ngram occurrence to be considered a motif, the marginal contribution due to the affinity of the prospective motif should at minimum exceed this penalty.


6: Run Structured Perceptron algorithm with decoded tag-sequences to update weights w 7: end for 8: return w The semi-supervised approach enables incorporation of significantly more training data.

this method could be used in conjunction with a supervised approach.


In general, these examples illustrate that the model can identify idiomatic and idiosyncratic themes as well as commonly recurrent ngrams (in the second example, the model picks out 'has become' which is highly recurrent, but doesn't have the semantic cohesiveness of some of the other motifs).

consider the second example, where the model picks 'white elephant' as a motif.


We believe that the approach has considerable theoretical as well as practical merits, and provides a simple and clean formulation for modeling phrasal and sentential semantics.

we believe that ours is the first method that can invoke different meaning representations for a token depending on textual context of the sentence.


We conjoin some of the feature sets by multiplying their values.

we add the cross product of the features of the category "Similarity" (see Table 1) with the rest of the features.


Second, it allows much more flexibility in defining the set of allowable LCs, H p .

h p may contain LCs of sizes greater than 1.


A tensor is a multidimensional array, and is a generalization of commonly used algebraic objects such as vectors and matrices.

a vector is a 1 st order tensor, a matrix is a 2 nd order tensor, and data organized as a rectangular cuboid is a 3 rd order tensor etc.


Therefore we expect the tensor model to be more effective in a low-resource training environment.

a vector space model assumes each feature weight to be a "free" parameter, and estimating them reliably could therefore be hard when training data are not sufficient or the feature set is huge.


The weight corresponding to the feature where w j h,i j is the i th j element in the vector w j h .

a true feature weight is now approximated by a set of bases.


The morphological generation step adds to the target graph all target word sequences from the monolingual data that map to the same stem sequence as one of the target phrases occurring in the baseline phrase table.

this step adds phrases that are morphological variants of existing phrases, differing only in their affixes.


Our working hypothesis is that the similarity between the discourse structures of an automatic and of a reference translation provides additional information that can be valuable for evaluating MT systems.

we believe that good translations should tend to preserve discourse relations.


Both the hidden layer and the output layer have 100 nodes 6 .

1 is a × 100 matrix and 2 a 100 × 100 matrix.


(2011) on the parallel bilingual training data described earlier.

bTLMPR is an extension to PLSA, and is the best performer among different versions of the bi-Lingual Topic Model (bLTM) described in Gao et al.


Our interpretation is that although CPTM provides a better smoothed estimation for low-frequent phrase pairs, which otherwise suffer the data sparsity issue, MRFp provides a more precise estimation for those high-frequent phrase pairs.

cPTM and MRFp capture complementary information for translation.


Secondly, our problem setting is different from the former studies, which usually deal with classification tasks and fine-tune the deep neural networks using training data with explicit class labels; in contrast we fine-tune our autoencoders using a semi-supervised criterion.

we use indirect supervision in the form of object classification in addition to the objective of reconstructing the attribute-centric input representation.


We finally build a stacked bimodal autoencoder (SAE) with all pre-trained layers and fine-tune them with respect to a semi-supervised criterion.

we unfold the stacked autoencoder and furthermore add a softmax output layer on top of the bimodal layery that outputs predictionst with respect to the inputs' object labels (e.g., boat): with weights W (6) ∈ R O×B , b (6) ∈ R O×1 , where O is the number of unique object labels.


A parser state s ∈ S is defined as a tuple s = (σ, β), where σ is a stack which is maintained to hold partial subtrees that are already constructed, and β is a queue which is used for storing word-POS pairs that remain unprocessed.

the initial state has an B0,3   c2,3   w2   A0,2   b1,2   w1   a0,1   w0 sh,sh,rr-A,sh,rl-B (a)   B0,3   F2,3   c2,3   w2   E0,2   A0,2   D1,2   b1,2   w1   C0,1   a0,1   w0 sh,ru-C,sh,ru-D,rr-A, ru-E,sh,ru-F,rl-B (b) Figure 1: Two constituent trees for an example sentence w 0 w 1 w 2 with POS tags abc.


MWE lexicons are exploited as sources of features for both the dependency parser and the external MWE analyzer.

two largecoverage general-language lexicons are used: the Lefff 6 lexicon (Sagot, 2010), which contains approximately half a million inflected word forms, among which approx.


Rule (c) is based on the assumption that obligatory cases are tied to the verb more strongly than optional cases.

obligatory cases cannot easily change their position.


(7) Enriching error case frames: The generated error cases are limited in error coverage because the procedures so far solely rely on preposition errors appearing in the learner corpus.

it is impossible to generate error case frames corresponding to preposition errors which do not appear in the learner corpus.


The MLE is then 4.3 Estimating D by leaving-one-out It would not be clear how to perform leavingone-out estimation on fractional counts, but here we have a distribution over realizations of the data, each with integral counts, and we can perform leaving-one-out estimation on each of these.

our goal is to find the D that maximizes the expected leaving-one-out loglikelihood, which is just the expected value of (3): where C is a constant not depending on D. We have made the assumption that the n r are independent.


In interpolated KN smoothing, the subtracted discounts are redistributed not only among unseen events but also seen events.

in this case, γ(u) is always equal to one, so that q u (w) = p ′ (w | u ′ ).


To reduce overfitting, we use expected KN smoothing during the M step.

during the E step, we calculate the distribution of c(e, f ) for each e and f , and during the M step, we train a language model on bigrams e f using expected KN smoothing (that is, with u = e and w = f ).


For learning, we iteratively adjust our model's parameters to better explain our samples.

we do unsupervised training via Monte Carlo EM.


As in , its name x.n is a stochastic transduction of its parent's name p.n.

is given by the probability that applying a random sequence of edits to the characters of p.n would yield x.n.


The Ψ x.p,x factors in (5) approximate Pr(p | z, i) (up to a constant factor), where p is the current phylogeny.

ψ x.p,x approximates the probability of a single edge.


We sample from Q using standard methods, similar to sampling from a linear-chain CRF by running the backward algorithm followed by forward sampling.

we run the sum-product algorithm from the leaves up to the root ♦, at each node x computing the following for each topic z: Then we sample from the root down to the leaves, first sampling ♦.z from β ♦ , then at each x = ♦ sampling the topic x.z to be z with probability proportional to Again we use IMH to correct for the bias in Q: we accept the resulting proposalẑ with probability While Pr(p, i,ẑ, x | θ, φ) might seem slow to compute because it contains many factors (1) with different denominators Z(x), one can share work by visiting the mentions x in their order i.


We incorporate Brown clusters into a regularizer in a similar way to the topical word groups inferred using LDA in §4.3, but here we make use of the hierarchy.

we construct treestructured groups, one per cluster (i.e., one per node in the hierarchy).


However, the improvements offered by our regularization methods can be applied in future work to other model families with more carefully engineered features, metadata features (especially important in forecasting), latent variables, etc.

note that other kinds of weights (e.g., metadata) can be penalized conventionally, or incorporated into the structured regularization where it makes sense to do so (e.g., n-grams, as in Nelakanti et al., 2013).


In fact, a similar argument to the one in the approximate analysis in Section 3.1 leads to an approximate formulaPP Mix ofPP Mix as when W is sufficiently large.

,PP Mix (W ′ ) also has a quadratic behavior in a log-log graph, i.e.,PP Mix (W ′ ) = O(W ′ ln W ′ ).


The intuition is to use the true label in D l when available, or otherwise resort to using the propagated label.

we now have the necessary ingredients to quantify the clustering consistency between a source Phy Per Emp Agt Aff GPE Dis N/A BC BN Nw CTS wL Figure 2: Heat map of the relevance scores w s, j between the target domain Usenet (UN) with the other domains on ACE 2004 data set.


We propose to perform joint inference upon multiple local predictions by leveraging implicit clues that are encoded with relation specific requirements and can be learnt from existing knowledge bases.

the joint inference framework operates on the output of a sentence level relation extractor as input, derives 5 types of constraints from an existing KB to implicitly capture the expected type and cardinality requirements for a relation's arguments, and jointly resolve the disagreements among candidate predictions.


Since we will focus on the open domain relation extraction, we still follow the distant supervision paradigm to collect our training data guided by a KB, and train the local extractor accordingly.

we train a sentence level extractor using the maximum entropy model.


Now, the issue is how to obtain the clues used in the previous subsection.

how we determine which relations expect certain types of subjects, which relations expect certain types of objects, etc.


We observe that for each approach, the optimal range is relatively stable.

for each approach, the amount of truly effective information about underlying semantic correlation keeps constant for the same dataset, which, to some extent, explains the reason why our approaches are robust to sparse features.


In Table 3, almost all BLEU scores are improved, no matter what strategy is used.

the best performance marked in bold is as high as 1.24, 0.94, and 0.82 BLEU points, respectively, over the baseline system on NIST04, CWMT08 Development, and CWMT08 Evaluation data.


Numerous classifiers are trained and each is an expert in translating a single word or phrase.

for each word type or phrase type that occurs as a fragment in the training set, and which does not map to just a single translation, a classifier is trained.


However, the utility of such a representation for summarization depends on the quality of pattern clusters.

event patterns must correspond to grammatically correct sentences.


At each level, we cluster the sentences by the method described above and choose the number of clusters k according to the gap statistic (Tibshirani et al., 2000).

for each level, the algorithm will cluster repeatedly with k varying from the minimum to the maximum.


The results of these experiments are shown in Figure 3, with the labelled attachment score 6 (LAS) for comparison.

5 the child nodes of a node are all processed before the node itself.


In our evaluation, we will contrast labelled accuracy, the standard parser evaluation metric, and our three α metrics.

we are interested in the correlation (or lack thereof) between LAS and the alphas, and whether the results of our synthetic experiments correspond well with the results on realworld IAA sets.


As can be seen, WiBi, as well as the page taxonomy of MENTA, is the resource with the best granularity, as not only does it attain high coverage, but it also provides a larger variety of classes as generalizations of pages and categories.

wiBi provides over 3M hypernym pages chosen from a range of 94k distinct hypernyms, while others exhibit a considerably smaller range of distinct hypernyms (e.g., DBpedia by design, but also wikiNet, with around 11k distinct page hypernyms).


Thus assuming there is an alignment model that is able to tell how likely one relation maps to the original question, we add extra alignment-based features for the incoming and outgoing relation of each node.

for each relation rel in a topic graph, we compute P (rel | question) to rank the relations.


Several of their proposed models rely on proprietary data; here we focus on LS models that rely on open-source data and frameworks.

we use the recurrent neural network language model (RNNLM) of Mikolov et al.


We implemented two such models, denoted DMM LS and DPM LS , by replacing the component that assigns argument labels with one that relies on LS.

as in §4.3, we compute the cosine similarity between the composite LS vectors of the question text and each marker argument (in DMM) or EDU (in DPM), and label the corresponding answer segment QSEG if this score is higher than a threshold, or OTHER otherwise.


FactChecker differs from prior approaches in that it does not rely on iterative peer voting, instead it leverages language to infer believability of fact candidates.

factChecker makes use of linguistic features to detect if a given source objectively states facts or is speculative and opinionated.


Note that in this paper we are interested in cases where τ (f i ) is either T or F .

we assess truthfulness of factual statements and not opinions whose truthfulness is often both T and F to some degree.


We look up entity types in a knowledge base containing entities and their types.

we use the NELL entity typing API (Carlson et al., 2010).


On the other hand, the cardinality of "INVERSE-OF(died in)" is many(n).

the number of places where a person can be born (one) is much fewer than the number of people that can die in a place (many).


In this paper, we presented a completely unsupervised method to detect word sense changes by analyzing millions of digitized books archived spanning several centuries.

we constructed DT networks over eight different time windows, clustered these networks and compared these clusters to identify the emergence of novel  senses.


They adopted the Nearest Neighbor (NN) and Information Bottleneck (IB) methods for clustering.

they tried to consider verb polysemy by using the IB method, which is a soft clustering method (Tishby et al., 1999).


To output multiple classes for each verb, we set a threshold, t, for class attribute probabilities.

classes that have a higher class attribute probability than the threshold are output for each verb.


Intuitively, however, latent tree models encode low rank dependencies among the observed variables permitting the development of "spec-tral" methods that can lead to provably correct solutions.

we leverage the concept of additive tree metrics (Buneman, 1971;Buneman, 1974) in phylogenetics and machine learning that can create a special distance metric among the observed variables as a function of the underlying spectral dependencies (Choi et al., 2011;Ishteva et al., 2012).


However, the fact that the z i are latent variables makes this strategy substantially more complicated.

it becomes challenging to compute the distances among pairs of latent variables.


We hypothesize that if a learner is able to associate words with the contexts of their use (as children likely are), this could provide a weak source of information for disambiguating minimal pairs even without knowing their exact meanings.

if the learner hears kV 1 t and kV 2 t in different situational contexts, they are likely to be different lexical items (and V 1 and V 2 different phones), despite the lexical similarity between them.


The TLD model retains the IGMM vowel phone component, but extends the lexicon of the LD model by adding topic-specific lexicons, which capture the notion that lexeme probabilities are topicdependent.

the TLD model replaces the Dirichlet Process lexicon with a Hierarchical Dirichlet Process (HDP; Teh (2006)).


SVO vs OSV), which must be done at a very young age anyway in order to extract meaning from language.

this model, trained on part-of-speech tags, represents the preferred locations of semantic roles relative to a verb as Gaussian mixtures over real numbers.


This work describes a cognitive model of the developmental timecourse of filler-gap comprehension with the goal of setting a lower bound on the modeling assumptions necessary for an ideal learner to display filler-gap comprehension.

the model described in this paper takes chunked child-directed speech as input and learns orderings over semantic roles.


Computational modeling provides a way to test the computational level of processing (Marr, 1982).

given the input (child-directed speech, adult-directed speech, and environmental experiences), it is possible to probe the computational processes that result in the observed output.


The model is most likely to hypothesize a preverbal object when it has already assigned the subject role to something and, in addition, there is no postverbal noun competing for the object label.

the model infers that an object extraction may have occurred if there is a 'missing' postverbal argument.


We present a method to jointly learn features and weights directly from distributional data in a log-linear framework.

we propose a non-parametric Bayesian model for learning phonological markedness constraints directly from the distribution of input-output mappings in an Optimality Theory (OT) setting.


The algorithm should be computationally cheap as well as accurate, so it fits well with active learning and other problems that require frequent iterations on large datasets.

we propose a novel non-linear distribution spreading algorithm, which first uses Delta IDF technique (Martineau and Finin, 2009) to weight features, and then leverages the distribution of Delta IDF scores of a feature across different classes to efficiently recognize discriminative features for the classification task in the presence of mislabeled data.


Following this idea, we develop computationally cheap feature weighting techniques to counteract such effect by boosting the weight of discriminative features, so that they would not be subdued and the instances with such features would have higher chance to be correctly classified.

we propose a non-linear distribution spreading algorithm for feature weighting.


The mechanism of Formula (3) is to nonlinearly spread out the distribution, so that the importance of class-specific features can be further boosted to counteract the effect of noisy labels.

according to Formula (3), a high (absolute value of) spread score indicates that the Delta IDF score of that term on that class is high and deviates greatly from the scores on other classes.


Specifically, according to Formula (3), a high (absolute value of) spread score indicates that the Delta IDF score of that term on that class is high and deviates greatly from the scores on other classes.

our algorithm assigns high spread score (absolute value) to a term on a class for which the term has strong discriminative power and very specific to that class compared with to other classes.


While phrase-level annotations do not improve baseline performance, the RNN model significantly benefits from these annotations because the phrases are themselves derived from nodes in the network structure.

the phrase annotations allow our best model to detect bias accurately in complex sentences that the baseline models cannot handle.


To capture the quality ("professionalness") of a translation, we take the average TER of the translation against each of our gold translations.

we define TER gold of translation t as where a lower TER gold is indicative of a higher quality (more professional-sounding) translation.


The impact of various extensions and smoothing techniques for language models is investigated in (Goodman, 2001;Goodman, 2000).

the authors compared Kneser-Ney smoothing, Katz backoff smoothing, caching, clustering, inclusion of higher order n-grams, sentence mixture and skip n-grams.


In order to align with standard language models the skip operator applied to the first word of a sequence will remove the word instead of introducing a wildcard.

the equation ∂ 1 w i i−n+1 = w i i−n+2 holds where the right hand side is the subsequence of w i i−n+1 omitting the first word.


We quantitatively study how earnings calls are correlated with the financial risks, with a special focus on the financial crisis of 2009.

we perform a text regression task: given the transcript of an earnings call, we predict the volatility of stock prices from the week after the call is made.


We focus on the second application, which has been fruitful for computer vision (Li Fei-Fei and Perona, 2005), computational biology (Perina et al., 2010), and information retrieval (Kataria et al., 2011).

we use topic models to aid statistical machine translation (Koehn, 2009, SMT).


Intuitively, we assume that all words can be projected to their hypernyms based on a uniform transition matrix.

given a word x and its hypernym y, there exists a matrix Φ so that y = Φx.


To better model the various kinds of hypernym-hyponym relations, we apply the idea of piecewise linear regression (Ritzema, 1994) in this study.

the input space is first segmented into several regions.


Specifically, the input space is first segmented into several regions.

all word pairs (x, y) in the training data are first clustered into several groups, where word pairs in each group are expected to exhibit similar hypernym-hyponym relations.


Intuitively, the interpretation with the highest probability is the interpretation with the lowest distance to satisfaction.

it is the interpretation that tries to satisfy all rules as much as possible.


In order to generate such a sentence, we need to identify which sentences from the original document should be extracted and combined to generate abstract sentences.

we want to identify the summaryworthy sentences in the text that can be combined into an abstract sentence.


Our extractive query-based method beats all other extractive systems with a higher ROUGE-1 and ROUGE-2 which shows the effectiveness of our utterance extraction model in comparison with other extractive models.

using our extractive model described in section 2.1, as a stand alone system, is an effective query-based extractive summarization model.


So self.action could be what action is being performed, self.parent could be the trigger's owner, and self.context could be the caller.

i was presenting a 3rd alternative where self referred to something specific to the trigger.


Reward Function: The reward function reflects the lecturers' preferences on summaries and is derived through linear regression analysis of a dataset containing lecturer constructed summaries and ratings of randomly generated summaries.

it is the following cumulative multivariate function: where X = {x 1 , x 2 , ..., x n } describes the combinations of the data trends observed in the timeseries data and a particular template.


where θ tok , θ bgr and θ dep are feature-based scoring functions for tokens, bigrams and dependencies respectively.

each is a feature map for a given variable type v ∈ {tok, bgr, dep} and w v is the corresponding vector of learned parameters.


Given the complexity and the novelty of the task, we exploit structural kernels to automatically engineer novel features.

we define an efficient tree kernel derived from the Partial Tree Kernel, (Moschitti, 2006a), suitable for encoding structural representation of comments into Support Vector Machines (SVMs).


Our structures are specifically adapted to the noisy usergenerated texts and encode important aspects of the comments, e.g., words from the sentiment lexicons, product concepts and negation words, which specifically targets the sentiment and comment type classification tasks.

our shallow tree structure is a two-level syntactic hierarchy built from word lemmas (leaves) and part-of-speech tags that are further grouped into chunks ( Fig.


In contrast, the STRUCT model relies on the fact that the negative word, destroy, refers to the PRODUCT (xoom) since they form a verbal phase (VP).

the tree fragment:  We perform OM on YouTube using supervised methods, e.g., SVM.


For example, it will generate the following subtree fragments:  To build a corpus of YouTube comments, we focus on a particular set of videos (technical reviews and advertisings) featuring commercial products.

we chose two product categories: automobiles (AUTO) and tablets (TABLETS).


Automatic keyphrase extraction concerns "the automatic selection of important and topical phrases from the body of a document" (Turney, 2000).

its goal is to extract a set of phrases that are related to the main topics discussed in a given document (Tomokiyo and Hurst, 2003;Liu et al., 2009b;Ding et al., 2011;Zhao et al., 2011).


Recall that the goal of keyphrase extraction is to identify the most representative phrases for a document.

if a candidate phrase c 1 is more representative than another candidate phrase c 2 , c 1 should be preferred to c 2 .


To fill this gap, we ran four keyphrase extraction systems on four commonly-used datasets of varying sources, including Inspec abstracts (Hulth, 2003), DUC-2001 news articles (Over, 2001), scientific papers (Kim et al., 2010b), and meeting transcripts (Liu et al., 2009a).

we randomly selected 25 documents from each of these 3 A more detailed analysis of the results of the SemEval-2010 shared task and the approaches adopted by the participating systems can be found in Kim et al.


An evaluation error occurs when a system outputs a candidate that is semantically equivalent to a gold keyphrase, but is considered erroneous by a scoring program because of its failure to recognize that the predicted phrase and the corresponding gold keyphrase are semantically equivalent.

an evaluation error is not an error made by a keyphrase extractor, but an error due to the naivety of a scoring program.


To accomplish this, we suggest exploiting an influential idea in the keyphrase extraction literature: the importance of a candidate is defined in terms of how related it is to other candidates in the text (see Section 3.3.1).

if we could relate an infrequent keyphrase to other candidates in the text, we could boost its importance.


We expect that desired improvements will come from usage in various NLP tasks, particularly word-sense disambiguation and semantic role labeling.

we anticipate that interaction with the NLP community will identify further enhancements, developments, and hints from usage.


First, the annotation task should be a central and natural action with familiar video game mechanics.

the annotation should be supplied by common actions such as collecting items, puzzles, or destroying objects, rather than through extrinsic tasks that players must complete in order to return to the game.


For the concept-concept dataset, V c is the union of V B c , which contains the lemmas of all synsets incident to c in BabelNet, and V n c , which contains novel lemmas derived from statistical associations.

novel lemmas were selected by computing the χ 2 statistic for co-occurrences between the lemmas of c and all other part of speech-tagged lemmas in Wikipedia.


Quality Enforcement Mechanisms Infection includes two game mechanics to limit adversarial players from creating many low quality annotations.

the game prevents players from both (1) allowing all humans to live, via the city infection level and (2) killing all humans, via survivors taking the player's gun; these actions would both generate many false positives and false negatives, respectively.


The productive feature engineering aspects of incorporating features into the discriminative MaxEnt classifier motivate the model choice for the problem at hand.

the ability of the MaxEnt model's estimation routine to handle overlapping (correlated) features makes it directly applicable to address the first limitation of the VSM model.


This paper is a contribution to this goal.

we address TLGTs, i.e., the types of HLT mentioned above that generate target language text.


set of fused suffixes from all the unique suffix sequences in the training data.

we simply pick characters from beginning of the word up to the first stem as the prefix and characters from the first suffix to the end of the word as the suffix.


Thus there is a lack of linguistic knowledge such as paradigm information (Stump, 2001) for each word category in our model.

all morphemes are treated the same in our model which is not true in natural languages.


The GP expression will be defined as a PR constraint in Section 3.3 that reflects the interactions between the graph and the CRFs model.

gP is integrated with estimation of parametric structural model.


The similarities are measured based on co-occurrence statistics over a set of predefined features (introduced in Section 4.1).

the point-wise mutual information (PMI) values, between vertices and each feature instantiation that they have in common, are summed to sparse vectors, and their cosine distances are computed as the similarities.


In this paper we use a basic neural network architecture and a lexicalized probability model to create a powerful MT decoding feature.

we introduce a novel formulation for a neural network joint model (NNJM), which augments an n-gram target language model with an m-word source window.


In this case, we infer the affiliation from the rule structure.

if unaligned target word t is on the right edge of an arc that covers source span [s i , s j ], we simply say that t is affiliated with source word s j .


Recall that our NNJM feature can be described with the following probability: This formulation lends itself to several natural variations.

we can reverse the translation direction of the languages, as well as the direction of the language model.


Therefore, we also present results using a simpler version of our decoder which emulates Chiang's original Hiero implementation (Chiang, 2007).

this means that we don't use dependency-based rule extraction, and our decoder only contains the following MT features: (1) rule probabilities, (2) n-gram Kneser-Ney LM, (3) lexical smoothing, (4) target word count, (5) concat rule penalty.


We assume that the score S(x, y) of each candidate dependency tree y ∈ Y(x) decomposes into a sum of "local" scores for arcs.

: where h → m is the head-modifier dependency arc in the tree y.


The resulting arc score By learning parameters U , V , and W that function well in dependency parsing, we also learn contextdependent embeddings for words and arcs.

u φ h (for a given sentence, suppressed) is an r dimensional vector representation of the word corresponding to h as a head word.


Moreover, by controlling the amount of information we can extract from each of the component feature vectors (via rank r), the statistical estimation problem does not scale dramatically with the dimensions of φ h , φ m and φ h,m .

the low-rank constraint can help generalize to unseen arcs.


In this way, our model is able to capture a wide range of information including the auxiliary features without having uncontrolled feature explosion, while still having the full accessibility to the manually engineered features that are proven useful.

we define the arc score s γ (h → m) as the combination where θ ∈ R L , U ∈ R r×n , V ∈ R r×n , and W ∈ R r×d are the model parameters to be learned.


We adopt a maximum soft-margin framework for this learning problem.

we find parameters θ, U , V , W , and {ξ i } that minimize where ŷ i −y i 1 is the number of mismatched arcs between the two trees, and ξ i is a non-negative slack variable.


However, to accelerate learning, we adopt an online learning setup.

we use the passive-aggressive learning algorithm (Crammer et al., 2006) tailored to our setting, updating pairs of parameter sets, (θ, U ), (θ, V ) and (θ, W ) in an alternating manner.


We use a low-rank version of B as the initialization.

we unfold the tensor B into a matrix B (h) of dimensions n and nd, where n = dim(φ h ) = dim(φ m ) and d = dim(φ h,m ).


∆θ and ∆U ) during each update but does not include any overall regularization.

keeping updating the model may lead to large parameter values and over-fitting.


Future work involves extending the tensor component to capture higher-order structures.

we would consider second-order structures such as grandparent-head-modifier by increasing the dimensionality of the tensor.


2 To preserve spatial information in the BoVW representation, we use the spatial pyramid technique (Lazebnik et al., 2006), which consists in dividing the image into several regions, computing BoVW vectors for each region and concatenating them.

we divide ESP images into 16 regions and the smaller CIFAR-100 images into 4.


As we show later, this procedure actually produces a set with better coverage than constructing logical forms recursively from spans of x, as is done in traditional semantic parsing.

for every span of x, we take at most 10 entities whose Freebase descriptions approximately match the span.


Recall that the WSABIE EMBEDDING model needs to estimate the label location in R m for each frame.

it must estimate 512 parameters based on at most 10 training examples.


The sense for a word token is defined as the most probable sense according to the per-document sense distribution G j estimated for the corresponding pseudo document that represents the surrounding context of the word token.


Based on this motivation, our directional models are also simultaneously trained.

our training encourages word embeddings to be consistent across alignment directions by introducing a penalty term that expresses the difference between embedding of words into an objective function.


(2013) have adapted a type of FFNN, i.e., CD-DNN-HMM (Dahl et al., 2012), to the HMM alignment model.

the lexical translation and alignment probability in Eq.


The model consists of a lookup layer, a hidden layer, and an output layer, which have weight  In our experiments, we merge distances that are greater than 8 and less than -8 into the special "≥8" and "≤-8" distances, respectively.

the hidden layer has weight matrices H } and computes y j using the corresponding matrices of the jump distance d. The Viterbi alignment is determined using the Viterbi algorithm, similar to the FFNN-based model, where the model is sequentially applied from f 1 to f J 5 .


The proposed RNN produces a single score that is constructed in the hidden layer by employing the distance-dependent weight matrices.

the computations in the hidden and output layer are as follows: where is an activation function, which is a hard hyperbolic tangent, i.e., htanh(x), in this study.


In this section, we extend the algorithm to allow incrementally re-introducing constraints.

we track which constraints are most often violated in order to explicitly enforce them in the algorithm.


, s n correspond to the individual words in τ .

each word in τ is considered as a candidate user selection.


Our first testset, labeled T ALL , is the resulting traffic-weighted multiset.

each test case d, s, τ appears k times, where k is the number of judges who selected τ in d. T ALL consists of 33,913 test cases.


HIM learns to recover anchor texts from their single word subselections.

hIM iteratively decides whether to expand the current selection (initially a single word) one word to the left or right via greedy binary decisions, until a stopping condition is met.


For this, we use fivefold cross-validation over our data collection described in Section 3.2.

to decode a fold with our meta-learner, we train ENS with the other four folds.


For T ALL the oracle performance is 87.3%.

our choice of ensemble members was able to recover a correct smart selection as a candidate in 87.3% of the user study cases.


For example, consider "abound", for which lexicographers of WordNet prescribe two different senses: • (v) abound: (be abundant of plentiful; exist in large quantities) • (v) abound, burst, bristle: (be in a state of movement or action) "The room abounded with screaming children"; "The garden bristled with toddlers" For the first sense, which is the most commonly used sense for "abound", the general overtone of the connotation would seem positive.

although one can use this sense in both positive and negative contexts, this sense of "abound" seems to collocate more often with items that are good to be abundant (e.g., "resources"), than unfortunate items being abundant (e.g., "complaints").


These factors are referred to as clique potentials in general MRFs, which are essentially functions that collectively determine the graph's joint probability.

let G = (V, E) denote a network of random variables, where V consists of the unobserved variables Y that need to be assigned values from label set L. Let Ψ denote a set of clique potentials that consists of two types of factors: Objective formulation Given an assignment y to all the unobserved variables Y and x to observed ones X (variables with known labels, if any), our objective function is associated with the following joint probability distribution where Z(x) is the normalization function.


Note that there is a difference in how humans judge the orientation and the degree of connotation for a given word out of context, and how the use of such words in context can be perceived as good/bad news.

we conjecture that humans may have a bias toward the use of positive words, which in turn requires calibration from the readers' minds (Pennebaker and Stone, 2003).


In particular, we conjecture that humans may have a bias toward the use of positive words, which in turn requires calibration from the readers' minds (Pennebaker and Stone, 2003).

we might need to tone down the level of positiveness in order to correctly measure the actual intended positiveness of the message.


The key difference, however, is that in our MRF representation, we can explicitly model various types of word-word, sense-sense and word-sense relations as edge potentials.

we can naturally encode relations that encourage the same assignment (e.g., synonym) as well as the opposite assignment (e.g., antonym) of the polarity labels.


We address this issue by learning sentimentspecific word embedding (SSWE), which encodes sentiment information in the continuous representation of words.

we develop three neural networks to effectively incorporate the supervision from sentiment polarity of text (e.g.


's data set may correspond to only one type of online deceptive opinion spam -fake reviews generated by people who have never been to offerings or experienced the entities.

according to their findings (Ott et al., 2011; has updated their guidelines on the use of endorsements and testimonials in advertising to suggest that posting deceptive reviews may be unlawful in the United States (FTC, 2009).


We explore the effect of both domain experts and crowdsourcing workers on intra-domain deception.

we reframe it as a intradomain multi-class classification task, where given the labeled training data from one domain, we learn a classifier to classify reviews according to their source, i.e., Employee, Turker and Customer.


We report both OvR performance and the performance of three One-versus-One binary classifiers, trained to distinguish between each pair of classes.

the three-class classifier is around 65% accurate at distinguishing between Employee, Customer, and Turker for each of the domains using Unigram, significantly higher than random guess.


We also observe that each of the three One-versus-One binary classifications performs significantly better than chance, suggesting that Employee, Customer, and Turker are in fact three different classes.

the two-class classifier is around 0.76 accurate in distinguishing between Turker and Employee reviews, despite both kinds of reviews being deceptive opinion spam.


Our modified SAGE model provides us with a tailored tool for this analysis.

each feature f is associated with a background value m f .


Participants, both Employees and Turkers, tend to exaggerate sentiment, and include more sentiment-related vocabularies in their lies.

positive deceptive reviews were generally more positive and negative deceptive reviews were more negative in sentiment when compared with the truthful reviews generated by actual customers.


We extend that line of work by directly analyzing and quantifying this bias, and by linking the results to methodological concerns in both NLP and psycholinguistics.

we predict human data from three widely used psycholinguistic experimental paradigms-lexical decision, word naming, and picture naming-using unigram frequency estimates from Google n-grams (Brants and Franz, 2006), Switchboard (Godfrey et al., 1992), spoken and written English portions of CELEX (Baayen et al., 1995), and spoken and written portions of the British National Corpus (BNC Consortium, 2007).


Similarly, Switchboard provides relatively little explanatory power over the other corpora in predicting picture naming RTs (blue bars), possibly because highly imageable nouns and verbs frequent in everyday interactions are underrepresented in telephone conversations between people with no common visual experience.

idiosyncratic facts about the topics, dialects, etc.


B-cubed and CEAF treat entities as sets of mentions and measure the agreement between key (or gold standard) entities and response (or system-generated) entities, while MUC and BLANC-gold are link-based.

mUC measures the degree of agreement between key coreference links (i.e., links among mentions within entities) and response coreference links, while non-coreference links (i.e., links formed by mentions from different entities) are not explicitly taken into account.


In this paper, we address the issues in scoring coreference partitions of predicted mentions.

we justify our decision to go back to the original scoring algorithms by arguing that manipulation of key or predicted mentions is unnecessary and could in fact produce unintuitive results.


It would be worthwhile to study the humanmachine correlation to see if what is difficult for a machine is also difficult for a human.

the goal is to show that the confidence scores of a sentiment classifier are negatively correlated with SAC.


The recursive neural models employ the semantic composition functions, which enables them to handle the complex compositionalities in sentiment analysis.

we propose a framework which learns to propagate the sentiments of words towards the target depending on context and syntactic structure.


For a vector, the softmax obtains the distribution over K classes.

the predicted distribution is y = softmax (Uv), where y is the predicted distribution, U ∈ R K×D is the classification matrix, and v is the vector representation of node.


As shown in the Table 1, AdaRNN achieves better results than the baselines.

we find that the performances of SVM-dep increase than SVM-indep.


As illustrated in Figure 3, the AdaRNN-w/oE and AdaRNN-w/E achieve the best accuracies at β = 2, and they have a similar trend.

β = 0 obtains a uniform distribution over the composition functions which does not help improve performances.


If the node is a preterminal node, we capture its lexical semantic by adding features indicating its WordNet sense information.

the first WordNet sense of the terminal word, and all this sense's hyponym senses will be added as features.


To assess the feature-enriched tree kernel, we evaluate our method on the ACE RDC 2004 corpus using the same experimental settings as (Qian et al., 2008).

we parse all sentences using the Charniak's parser (Charniak, 2001), relation instances are generated by iterating over all pairs of entity mentions occurring in the same sentence.


Based on the assumption that querying a (pre-indexed) document collection is relatively simple, we generate a range of queries of decreasing length and increasing likelihood of term co-occurrence in standard documents, and query until a non-empty set of results is returned.

the modified support formulation is as follows: is a Boolean function which evaluates to true iff w i and w j co-occur in document d. we reject any combinations of words which are found to co-occur in Wikipedia documents for that language.


For research purposes, we have obtained free access to the Microsoft Translator, which supports translations among 44 languages.

given public Wordnets aligned to the PWN ( such as the FinnWordNet (FWN) (Lindén, 2010) and the JapaneseWordNet (JWN)  ) and the Microsoft Translator, we build Wordnet synsets for arb, asm, dis, ajz and vie.


No other modification of the tree is allowed.

we use the same set of tags and predicate labels in the nonleaf nodes and do not use new tags for the Turkish trees.


Each font style token f i takes on a value in {ITALIC, NON-ITALIC} and is generated conditioned on the previous font style f i−1 and the current character token e i .

after generating a character token that is not a space, the language model deterministically generates the last font used.


As a result, we are free to use inference procedures that are specialized for Viterbi computation.

we use beam-search with estimated forward costs.


We evaluated the learned styles by measuring how accurately OCULAR-BEAM-IT was able to distinguish between italic and non-italic styles.

we computed the precision and recall for the system's predictions about which words were italicized.


With this observation, we can simply modify the feature matrix and parameter vector and train the logistic model as usual.


There is evidence in the literature that certain tissue samples may have been cross-contaminated.

5 tumor and 4 normal samples should have their labels flipped.


As our last baselines, we use two methods based on syntactic pre-or post-ordering, which are state-ofthe-art methods for the language pairs.

for en-ja translation we use the head finalization pre-ordering method of (Isozaki et al., 2010b), and for ja-en translation, we use the syntactic postordering method of (Goto et al., 2012).


(2013), which has an alignment model that is parameterised in its original form solely on the variable λ.

the probability of a sentence e given a sentence f is given as: here, m is the length of the target sentence e, n the same for source sentence f , δ is the alignment model and θ is the translation model.


In contrast, our rule set is for Chinese-English PBSMT.

the direction of translation is opposite.


Among the latter, Ferron and Massa (2014) analyze 88 articles and their discussion pages related to traumatic events.

they find a correlation between the article edits and their discussions around the anniversaries of the events.


Experimental results on standard SIGHAN 2003 and 2005 bake-off evaluations show that our model outperforms the character and word baselines by a significant margin.

out approach improves OOV recall rates and segmentation consistency, and gives the best reported results to date on 6 out of 7 datasets.


Segmentation is a common practice in Arabic NLP due to the language's morphological richness.

clitic separation has been shown to improve performance on Arabic parsing (Green and Manning, 2010) and Arabic-English machine translation (Habash and Sadat, 2006).


Third, we show that dialectal data can be handled in the framework of domain adaptation.

we show that even simple feature space augmentation (Daumé, 2007) yields significant improvements in task accuracy.


A notable property of this feature set is that it remains highly dialect-agnostic, even though our additional features were chosen in response to errors made on text in Egyptian dialect.

it does not depend on the existence of a dialectspecific lexicon or morphological analyzer.


Of the annotation inconsistencies that do not involve typographical errors, a handful are segmentation mistakes; in most cases, however, the segmentation is justifiable but arbitrary.

a few colloquial "filler" expressions are sometimes not segmented, despite containing Arabic clitics that are segmented elsewhere in the data.


Recent experimental evidence (Reddy et al., 2011; suggests that for a number of compositional models the introduction of a disambiguation step prior to the actual compositional process results in better composite representations.


Based on the results of step (a)&(b), we calculate the word distributions of microblogs as follows: where P (w e i |d m j ) represents the probability that word w e i will appear in microblog d m j .

though some words may not actually appears in a microblog, there is still a probability that it is highly relevant to the microblog.


Traditionally, the evaluation of speech recognition systems has been carried by preparing a test set of annotated utterances and comparing the accuracy of a system's transcripts of those utterances against the annotations.

we seek to measure and minimize the word error rate (WER) of a system, with a WER of zero indicating perfect transcription.


For example, in the sentence fragment mustat kissat juoksevat (black cats are running), the words mustat and kissat share the plural number.

the analyses of both mustat and kissat are required to contain the sub-label PLURAL.


We will see that the success of such classifiers are strongly correlated with the quality of the underlying machine translation system.

given a corpus consisting of both machine-translated English text (English being the target language) and native English text (not necessarily the reference translation of the machine-translated text), we measure the accuracy of the system in classifying the sentences in the corpus as machine-translated or not.


Some of their models are also Tverskyan ratio models.

to see this, we divide (9) everywhere by σ(w 1 , w 2 ): If the SI is MIN, then the two terms in the denominator are the inverses of what W&W call difference-weighted precision and recall: REC(w 1 , w 2 ) = σMIN (w 1 ,w 2 ) σMIN (w 2 ,w 2 ) , So for t MIN , (9) can be rewritten: t MIN is a weighted harmonic mean of precision and recall, the so-called weighted Fmeasure (Manning and Schütze, 1999).


In this work, we generalize the SKIP-GRAM model, and move from linear bag-of-words contexts to arbitrary word contexts.

following work in sparse vector-space models (Lin, 1998;Padó and Lapata, 2007;Baroni and Lenci, 2010), we experiment with syntactic contexts that are derived from automatically produced dependency parse-trees.


The different kinds of contexts produce noticeably different embeddings, and induce different word similarities.

the bag-ofwords nature of the contexts in the "original" SKIPGRAM model yield broad topical similarities, while the dependency-based contexts yield more functional similarities of a cohyponym nature.


Interestingly, the most discriminative syntactic contexts in these cases are 5 Additional experiments (not presented in this paper) reinforce our conclusion.

we found that DEPS perform dramatically worse than BOW contexts on analogy tasks as in (Mikolov et al., 2013c;.


They are joined by other distantly related items in later periods, although by no more than a handful in each.

the construction is markedly less productive in these outer domains, which never form proper clusters of verbs.


Our method generates a summary by trimming a nested tree.

we extract a rooted document subtree from the document tree, and sentence subtrees from sentence trees in the document tree.


This meant that our method generated a summary with a significantly smaller number of sentences 6 .

our method relaxed fragmentation without decreasing the ROUGE score.


We present a model based on Bayesian surprise which provides an intuitive way to identify surprising information from a summarization input with respect to a background corpus.

the method quantifies the degree to which pieces of information in the input change one's beliefs' about the world represented in the background.


By exploiting the redundancy in human languages, we tackle the problem using a new three-view tri-training algorithm (TTA).

we first represent each document in three distinct views, and then tri-train three classifiers in these views.


In contrast, besides the character n-gram view, we also make use of the lexical and syntactic views.

three distinct views are used for building classifiers.


"...compared with previous approaches (Author (2005), Author and Author (2007))"), the first n elements of the list of suggested documents all count as the first element.

if any of the references in a multiple citation of n elements appears in the first n positions of the list of suggestions, it counts as a successful resolution and receives a score of 1.


Contributors were very satisfied with the task (4.5 on a scale from 1 to 5).

they felt instructions were clear (4.4/5), and that the pay was reasonable (4.1/5).


• Extrinsic Evaluation -We elucidate the sentiment consistency of entities reported in different language editions of Wikipedia using our propagated lexicons.

we pick 30 languages and compute sentiment scores for 2,000 distinct historical figures.


limited use of informal words), but is representative and convenient over a large number of languages.

we collect total of 7,741,544 high-frequency words from 136 languages to serve as vertices in our graph.


• Machine Translation -We script the Google translation API to get even more semantic links.

we ask for translations of each word in our English vocabulary to 57 languages with available translators as well as going from each known vocabulary word in other languages to English.


Table 2 reveals that very sparse sentiment lexicons resulted for a small but notable fraction of the languages we analyzed.

only 20 languages yielded lexicons of less than 100 words.


3 Experimental Setup The objective of the study is to determine the usefulness of instances of various types in the training data for semantic novelty classification.

in light of , we want to check whether the presence of less reliable data (hard cases) in the training set adversely impacts performance on the highly reliable data (easy cases).


The system trained solely on easy cases (E in Table 3) significantly outperforms H both on the easy and on the hard test.

easy cases are more informative about the classification of hard cases than the hard cases themselves.


It is apparent from the figure that the difference in the distributions of the easy and the hard cases lead to a lower threshold for LSA score when WordNet score is zero and a higher threshold of WordNet score when LSA score is zero in hard vs easy cases.

the system exposed to hard cases learned to trust LSA more and to trust Word-Net less when determining that an instance is semantically old than a system that saw only easy cases at train time.


VerbNet (Kipper et al., 2008; based on Levin, 1993) lists over 6,000 verbs, categorized into 280 classes according to the syntactic frames they can appear in.

all verbs in the same class appear in the same set of syntactic frames.


Given the prominence of the Semantic Consistency Hypothesis in both theory and practice, one might expect that it was on firm empirical footing.

ideally there would be some database of semantic judgments for a comprehensive set of verbs from each syntactic class.


1 In the aftermath, Chinese media accused Western media of "softpedaling the attack and failing to state clearly that it was an act of terrorism".

2 regarding the statement by the US embassy that referred to this incident as the "terrible and senseless act of violence in Kunming", a Weibo user posted "If you say that the Kunming attack is a 'terrible and senseless act of violence', then the 9/11 attack can be called a 'regrettable traffic incident"'.


• We use a combination of regular expressions and dependency rules to capture expressions of the type "CLAIM, according to SOURCE."

the PCOMP path from according is searched for the pattern according to * .


As an easy observation, the emotion lexicon generated by the EaLDA model consistently and significantly outperforms the WordNet-Affect emotion lexicon and other three emotion classification systems.

we are able to obtain an overall F1-score of 10.52% for disgust classification task which is difficult to work out using pre-viously proposed methods.


We run a new set of experiments in which the reservoir size is held fixed at 1000 and the size of the initialization sample is varied.

we vary the size of the initialization sample, in documents, between zero (corresponding to no Gibbs initialization), 30, 100, and 300, and also perform a run of batch Gibbs sampling (with no particle filter).


This enabled a direct comparison of state-of-the-art NEL and wikification systems, highlighting the effect of key differences.

nER and coreference modules make nEL approaches suitable for applications that require all mentions, including ambiguous names and entities that are not in the KB.


The core idea of LSH is that if two data points are close, then after a "projection" operation they will remain close.

similar data points are more likely to be mapped into the same bucket with a high collision probability.


The resulting event graph for a document is strongly connected, but not complete.

the following edge types are included: 1.


Moreover, a quantitative analysis of tag confusions reveals that the majority of disagreements are due to linguistically debatable cases rather than annotation errors.

we show that even in the absence of annotation guidelines only 2% of annotator choices are linguistically unmotivated.


Disagreements are very similar across all three domains.

adpositions (ADP) are confused with particles (PRT) (as in the case of "get out"); adjectives (ADJ) are confused with nouns (as in "stone lion"); pronouns (PRON) are confused with determiners (DET) ("my house"); numerals are confused with adjectives, determiners, and nouns ("2nd time"); and adjectives are confused with adverbs (ADV) ("see you later").


Reddit is a good corpus for the irony detection task in part because it provides a natural practical realization of the otherwise ill-defined context for comments.

each comment is associated with a specific user (the author), and we can view their previous comments.


We fit this model to the annotated corpus, and found a significant correlation:β 1 = 1.508 with a 95% confidence interval of (1.326, 1.690); p < 0.001.

annotators request context significantly more frequently for those comments that (are ultimately deemed to) contain an ironic sentence.


In the extraordinarily rich literature on WSD, we focus our review on those closest to the topic of Lesk and NBM.

we opt for the "simplified Lesk" (Kilgarriff and Rosenzweig, 2000), where inventory senses are assessed by glosscontext overlap rather than gloss-gloss overlap.


Hyponyms, on the other hand, help specify their corresponding senses with information that is possibly missing from the often overly brief glosses: the many technical terms as hyponyms in Table 1 -though rare -are likely to occur in the (possibly domain-specific) contexts that are highly typical of the corresponding senses.

for the NBM, the co-occurrence is likely to result in stronger gloss-definition associations when similar contexts appear in a WSD instance.


To avoid underflow, Equation (1) is estimated as the following log probability: where c(x) is the count of word x, c(•) is the corpus size, c(x, y) is the joint count of x and y, and |v| is the dimension of vector v. Nonetheless, we do investigate how model performance responds to estimation quality.

in WSD, a source corpus is defined as the source of the majority of the WSD instances in a given dataset, and a baseline corpus of a smaller size and less resemblance to the instances is used for all datasets.


mance benefits most from hyponyms and least from hypernyms.

highly similar, finegrained sense candidates apparently share more hypernyms in the fine-grained case than in the coarse-grained case; adding to the generality of hypernyms (both semantic and distributional), we postulate that their probability in the NBM is uniformly inflated among many sense candidates, and hence they decrease in distinguishability.


(2012) propose to use a set of pivot features, and train the autoencoder to reconstruct the pivots from the full set of features.

the corrupted input is divided to S subsets We obtain a projection matrix W s for each subset by reconstructing the pivot features from the features in this subset; we can then use the sum of all reconstructions as the new features, tanh( S s=1 W s X s ).


To achieve this, we introduce a constraint that all positions that have similar characteristics must be selected at the same time.

we first group all positions in the source sentences using features of the position, and introduce a constraint that all positions with identical features must be selected at the same time.


In addition, because all positions can be classified as either segmented or not by evaluating whether the corresponding feature is in the learned feature set or not, it is not necessary to train an additional classifier for the segmentation model when using this algorithm.

this constraint conducts a kind of feature selection for greedy search.


In this paper we instead explicitly model the translation problem with sentence skeleton information.

• We develop a skeleton-based model which divides translation into two sub-models: a skeleton translation model (i.e., translating the key elements) and a full translation model (i.e., translating the remaining source words and generating the complete translation).


Our caption generation system effectively leverages information from the massive amounts of human-written image captions on the internet.

it exhibits strong performance on the SBU-Flickr dataset (Ordonez et al., 2011), a noisy corpus of one million captioned images collected from the web.


These corpora all provide experts' corrections along with error Type name description A gap-between-edits Gap between the two edits.

we use the number of words between the two edits' original words, as well as the revised words.


The improvement is most noticeable on FCE corpus, where the error in correction detection step was reduced by 9%.

one third of the correction misdetections were eliminated.


Table 5 shows that the number of merging errors are significantly reduced by the new merging algorithm.

the number of false positives (system proposes merges when it should not) is significantly reduced.


Here the hypothesis is that friends, who usually share common interests, tend to discuss similar topics and use similar words than non-friends.

we believe that a cold start user's language model can be enriched and better personalized by incorporating content written by friends.


Due to the noise inevitably contained in social media content, instead of using all available documents, we argue that by properly specifying the set of relevant documents, a better personalized language model can be learnt.

each user language model is enriched by a personalized collection of background documents.


Given the set of documents C relevant to topic k, we proposed to generate a label of a desired length x from the summarisation of C. We compare different summarisation algorithms based on their ability to provide a good label to a given topic.

we investigate the use of lexical features by comparing three different wellknown multi-document summarisation algorithms against the top-n topic terms baseline.


We used the OpenCalais' document categorisation service 1 to generate categorical sets.

we considered four different categories which contain many real-world events, namely: War and Conflict (War), Disaster and Accident (DisAc), Education (Edu) and Law and Crime (LawCri).


We can see in all four categories that the SB and TFIDF approaches provide a better summarisation coverage as the length of the topic label increases.

in both the Education and Law & Crime categories, both SB and TFIDF outperforms TT and TR by a large margin.


Table 1 presents average results for ROUGE-1 in the four categories.

the SB and TFIDF summarisation techniques consistently outperform the TT baseline across all four categories.


Other non-edit states are constructed only when they are reachable from another state.

a DELETE or SUBST arc always transitions to a non-edit state, since it consumes one of the lookahead characters.


We will apply different types of semantic information to three dependency parsers.

we will test the following questions: • Does semantic information in WordNet help dependency parsing?


To combat this, we introduce a secondary beam for each unique unlabeled structure.

we partition the primary (entire) beam into disjoint groups according to the identity of unlabeled structure.


For the Penn-S-3.3.0 results we can see that our model outperforms Tur-boPaser and is competitive with the Berkeley constituency parser (Petrov et al., 2006).

if gold tags are assumed, cube-pruning significantly outperforms Berkeley.


Aggarwal (2006) studied this problem and showed that by altering the sampling probability (f (n, k) in Algorithm 1) in the reservoir sampling algorithm, it is possible to achieve different agerelated biases in the sample.

he showed that by setting the sampling probability to the constant function f (n, k) = k/β, it is possible to approximately achieve exponential bias in the sample with scale parameter β (Aggarwal, 2006).


A combination of topic, discussion, and sentiment features achieves the best performance on recall, F1, and accuracy.

the accuracy is significantly higher than all the other systems (paired-t test, p < 0.05).


From Table 4 we can see that overall the system achieves 66% of the human performance with comparable stability as human.

method 4 based on translation and transliteration generates much more perceivable morphs than human because the system may search in a larger vocabulary.


Thus, we are motivated to develop a portable technique that generates representations of semantic content that are richer than keywords, and that can be applied to broad domains.

we seek to extract important information structures from an unannotated corpus comprising texts of the same genre and relating to the same domain.


Without a thorough evaluation we cannot make strong claims.

we have little sense of the technique's recall, i.e.


In this paper, we propose a new distant supervision method, called Semantic Consistency, which can identify reliable instances from noisy instances by inspecting whether an instance is located in a semantically consistent region.

we propose a semantic consistency model, which first models the local subspace around an instance as a sparse linear combination of training instances, then estimate the semantic consistency by exploiting the characteristics of the local subspace.


To model the semantic consistency, this paper proposes a local subspace based method.

given sufficient training instances, our method first models each relation type as a linear subspace spanned by its training instances.